Browsing by Author "Parekh, Tejas"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Clinical Deployment of Explainable Artificial Intelligence of SPECT for Diagnosis of Coronary Artery Disease
    (Elsevier, 2022) Otaki, Yuka; Singh, Ananya; Kavanagh, Paul; Miller, Robert J. H.; Parekh, Tejas; Tamarappoo, Balaji K.; Sharir, Tali; Einstein, Andrew J.; Fish, Mathews B.; Ruddy, Terrence D.; Kaufmann, Philipp A.; Sinusas, Albert J.; Miller, Edward J.; Bateman, Timothy M.; Dorbala, Sharmila; Di Carli, Marcelo; Cadet, Sebastien; Liang, Joanna X.; Dey, Damini; Berman, Daniel S.; Slomka, Piotr J.; Medicine, School of Medicine
    Background: Explainable artificial intelligence (AI) can be integrated within standard clinical software to facilitate the acceptance of the diagnostic findings during clinical interpretation. Objectives: This study sought to develop and evaluate a novel, general purpose, explainable deep learning model (coronary artery disease-deep learning [CAD-DL]) for the detection of obstructive CAD following single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). Methods: A total of 3,578 patients with suspected CAD undergoing SPECT MPI and invasive coronary angiography within a 6-month interval from 9 centers were studied. CAD-DL computes the probability of obstructive CAD from stress myocardial perfusion, wall motion, and wall thickening maps, as well as left ventricular volumes, age, and sex. Myocardial regions contributing to the CAD-DL prediction are highlighted to explain the findings to the physician. A clinical prototype was integrated using a standard clinical workstation. Diagnostic performance by CAD-DL was compared to automated quantitative total perfusion deficit (TPD) and reader diagnosis. Results: In total, 2,247 patients (63%) had obstructive CAD. In 10-fold repeated testing, the area under the receiver-operating characteristic curve (AUC) (95% CI) was higher according to CAD-DL (AUC: 0.83 [95% CI: 0.82-0.85]) than stress TPD (AUC: 0.78 [95% CI: 0.77-0.80]) or reader diagnosis (AUC: 0.71 [95% CI: 0.69-0.72]; P < 0.0001 for both). In external testing, the AUC in 555 patients was higher according to CAD-DL (AUC: 0.80 [95% CI: 0.76-0.84]) than stress TPD (AUC: 0.73 [95% CI: 0.69-0.77]) or reader diagnosis (AUC: 0.65 [95% CI: 0.61-0.69]; P < 0.001 for all). The present model can be integrated within standard clinical software and generates results rapidly (<12 seconds on a standard clinical workstation) and therefore could readily be incorporated into a typical clinical workflow. Conclusions: The deep-learning model significantly surpasses the diagnostic accuracy of standard quantitative analysis and clinical visual reading for MPI. Explainable artificial intelligence can be integrated within standard clinical software to facilitate acceptance of artificial intelligence diagnosis of CAD following MPI.
  • Thumbnail Image
    Item
    Prognostic Value of Phase Analysis for Predicting Adverse Cardiac Events beyond Conventional SPECT Variables: Results from the REFINE SPECT Registry
    (American Heart Association, 2021) Kuronuma, Keiichiro; Miller, Robert J. H.; Otaki, Yuka; Van Kriekinge, Serge D.; Diniz, Marcio A.; Sharir, Tali; Hu, Lien-Hsin; Gransar, Heidi; Liang, Joanna X.; Parekh, Tejas; Kavanagh, Paul; Einstein, Andrew J.; Fish, Mathews B.; Ruddy, Terrence D.; Kaufmann, Philipp A.; Sinusas, Albert J.; Miller, Edward J.; Bateman, Timothy M.; Dorbala, Sharmila; Di Carli, Marcelo; Tamarappoo, Balaji K.; Dey, Damini; Berman, Daniel S.; Slomka, Piotr J.; Radiation Oncology, School of Medicine
    Background: Phase analysis of single-photon emission computed tomography myocardial perfusion imaging provides dyssynchrony information which correlates well with assessments by echocardiography, but the independent prognostic significance is not well defined. This study assessed the independent prognostic value of single-photon emission computed tomography-myocardial perfusion imaging phase analysis in the largest multinational registry to date across all modalities. Methods: From the REFINE SPECT (Registry of Fast Myocardial Perfusion Imaging With Next Generation SPECT), a total of 19 210 patients were included (mean age 63.8±12.0 years and 56% males). Poststress total perfusion deficit, left ventricular ejection fraction, and phase variables (phase entropy, bandwidth, and SD) were obtained automatically. Cox proportional hazards analyses were performed to assess associations with major adverse cardiac events (MACE). Results: During a follow-up of 4.5±1.7 years, 2673 (13.9%) patients experienced MACE. Annualized MACE rates increased with phase variables and were ≈4-fold higher between the second and highest decile group for entropy (1.7% versus 6.7%). Optimal phase variable cutoff values stratified MACE risk in patients with normal and abnormal total perfusion deficit and left ventricular ejection fraction. Only entropy was independently associated with MACE. The addition of phase entropy significantly improved the discriminatory power for MACE prediction when added to the model with total perfusion deficit and left ventricular ejection fraction (P<0.0001). Conclusions: In a largest to date imaging study, widely representative, international cohort, phase variables were independently associated with MACE and improved risk stratification for MACE beyond the prediction by perfusion and left ventricular ejection fraction assessment alone. Phase analysis can be obtained fully automatically, without additional radiation exposure or cost to improve MACE risk prediction and, therefore, should be routinely reported for single-photon emission computed tomography-myocardial perfusion imaging studies.